- Pisano ED
- Gatsonis C
- Hendrick E
- et al.
Reduced lung-cancer mortality with low-dose computed tomographic screening.
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- Diagnostic performance of digital versus film mammography for breast-cancer screening.N Engl J Med. 2005; 353: 1773-1783
- Reduced lung-cancer mortality with low-dose computed tomographic screening.N Engl J Med. 2011; 365: 395-409
- Global trend in artificial intelligence–based publications in radiology from 2000 to 2018.Am J Roentgenol. 2019; 213: 1204-1206
- Design characteristics of studies reporting the performance of artificial intelligence algorithms for diagnostic analysis of medical images: results from recently published papers.Korean J Radiol. 2019; 20: 405-410
- A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis.Lancet Digital Health. 2019; 1: e271-e297
- Randomized clinical trials of artificial intelligence in medicine: Why, When, and How?.Korean J Radiol. 2022; 23: 1119-1125
- Artificial intelligence in breast cancer screening: evaluation of FDA device regulation and future recommendations.JAMA Intern. Med. 2022; 182: 1306-1312
AI Central. Available at: https://aicentral.acrdsi.org Accessed February 16, 2023.
- Treating medical data as a durable asset.Nat Genet. 2020; 52: 1005-1010
- Federated learning in medical imaging: Part I: toward multicentral health care ecosystems.J Am College Radiol. 2022; 19: 969-974
- Mitigating bias in radiology machine learning: 1. Data handling. Radiology.Artificial Intelligence. 2022; 24 (:e210290): 5
Center for Biomedical Image Computing &Analytics. RSNA-ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge 2021. http://braintumorsegmentation.org/. Accessed February 16, 2023.
National Cancer Institute. Cancer imaging archive. Available at: https://www.cancerimagingarchive.net. Accessed February 16, 2023
ACR DSI Dataset Directory. Available at: https://www.acrdsi.org/DSI-Services/Dataset-Directory. Accessed February 16, 2023
Medical imaging data resource center (MIDRC). Available at: https://www.midrc.org. Accessed February 16, 2023
- Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data.Sci Rep. 2020; 10: 1-2
- Privacy-first health research with federated learning.NPJ Digital Med. 2021; 4: 1-8
Roth HR, Chang K, Singh P, et al. Federated learning for breast density classification: A real-world implementation. InDomain Adaptation and Representation Transfer, and Distributed and Collaborative Learning: Second MICCAI Workshop, DART 2020, and First MICCAI Workshop, DCL 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4-8, 2020, Proceedings 2 2020 (pp. 181-191). Springer International Publishing.
- Federated Learning used for predicting outcomes in SARS-COV-2 patients.Res Square. 2021;
- Distributed deep learning networks among institutions for medical imaging.J Am Med Inform Assoc. 2018; 25: 945-954
- Federated learning in medical imaging: Part II: methods, challenges, and considerations.J Am College Radiol. 2022; 19: 975-982
- Leveraging federated learning to promote deep learning model development.J Am College Radiol. 2022; 19: 967-968
Available at: https://dart.acr.org. Accessed February 16, 2023.
- External COVID-19 deep learning model validation on ACR AI-LAB: It's a Brave New World.J Am College Radiol. 2022; 19: 891-900
Available at: https://ailab.acr.org. Accessed February 16, 2023.
- ACR’s Connect and AI-LAB technical framework.JAMIA open. 2022; 5: ooac094
- Advances and open problems in federated learning.Foundations Trends® Machine Learn. 2021; 14: 1-210